LangGraph vs Windmill
Side-by-side comparison of two AI agent tools
LangGraphopen-source
Build resilient language agents as graphs.
Windmillopen-source
Open-source developer platform to power your entire infra and turn scripts into webhooks, workflows and UIs. Fastest workflow engine (13x vs Airflow). Open-source alternative to Retool and Temporal.
Metrics
| LangGraph | Windmill | |
|---|---|---|
| Stars | 42.5k | 18.1k |
| Star velocity /mo | 2.4k | 317.80748663101605 |
| Commits (90d) | 129 | 1.2k |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8817860900670718 | 0.8726503315063926 |
Pros
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
- +Multi-language support with automatic UI generation from scripts in Python, TypeScript, Go, Bash, SQL, and more
- +High performance workflow engine claiming 13x faster execution than Airflow
- +Self-hostable open-source solution with AGPLv3 license providing full control and customization
Cons
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
- -AGPLv3 license may restrict some commercial use cases and require careful compliance consideration
- -Being a comprehensive platform may introduce complexity for simple automation tasks
- -Self-hosting requires infrastructure management and maintenance overhead
Use Cases
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions
- •Building internal APIs and webhooks from existing scripts without additional infrastructure
- •Creating automated workflows for background jobs and data processing pipelines
- •Developing low-code internal applications with custom UIs for non-technical team members